# Defense Against the Dark Arts: An overview of adversarial example security research and future research directions

arXiv: https://arxiv.org/abs/1806.04169 · HTML: https://mlsanity.com/paper/1806.04169/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 11 of 11)

- [Adversarial Token Attacks on Vision Transformers](https://mlsanity.com/paper/2110.04337/) (2021)
- [PubDef: Defending Against Transfer Attacks From Public Models](https://mlsanity.com/paper/2310.17645/) (2023)
- [Careful What You Wish For: on the Extraction of Adversarially Trained Models](https://mlsanity.com/paper/2207.10561/) (2022)
- [Towards Making Deep Learning-based Vulnerability Detectors Robust](https://mlsanity.com/paper/2108.00669/) (2021)
- [Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing](https://mlsanity.com/paper/2311.09024/) (2023)
- [IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context](https://mlsanity.com/paper/2510.02742/) (2025)
- [Smooth-Reduce: Leveraging Patches for Improved Certified Robustness](https://mlsanity.com/paper/2205.06154/) (2022)
- [Robusta: Robust AutoML for Feature Selection via Reinforcement Learning](https://mlsanity.com/paper/2101.05950/) (2021)
- [Evaluating Adversarial Robustness in the Spatial Frequency Domain](https://mlsanity.com/paper/2405.06345/) (2024)
- [How can we trust opaque systems? Criteria for robust explanations in XAI](https://mlsanity.com/paper/2508.12623/) (2025)
- [DivQAT: Enhancing Robustness of Quantized Convolutional Neural Networks against Model Extraction Attacks](https://mlsanity.com/paper/2512.23948/) (2025)

## Abstract (arXiv)

This article presents a summary of a keynote lecture at the Deep Learning Security workshop at IEEE Security and Privacy 2018. This lecture summarizes the state of the art in defenses against adversarial examples and provides recommendations for future research directions on this topic.